Preference for art: similarity, statistics, and selling price

Graham, Daniel J.; Friedenberg, Jay; McCandless, Cyrus H.; Rockmore, Daniel N.

doi:10.1117/12.842398

Cited by 18 publications

(20 citation statements)

References 36 publications

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“…This observation supports the hypothesis put forward in Graham et al [22] where it is suggested that aesthetic experience, associated with human content, may rely on specific qualities of the artwork that are different from the structural features characterizing visual patterns lacking human forms. This result also supports the idea that, in the absence of a human figure, low-level visual features predominantly affect the visual scan path.…”

Section: Discussionsupporting

confidence: 90%

When Art Moves the Eyes: A Behavioral and Eye-Tracking Study

et al. 2012

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The aim of this study was to investigate, using eye-tracking technique, the influence of bottom-up and top-down processes on visual behavior while subjects, naïve to art criticism, were presented with representational paintings. Forty-two subjects viewed color and black and white paintings (Color) categorized as dynamic or static (Dynamism) (bottom-up processes). Half of the images represented natural environments and half human subjects (Content); all stimuli were displayed under aesthetic and movement judgment conditions (Task) (top-down processes). Results on gazing behavior showed that content-related top-down processes prevailed over low-level visually-driven bottom-up processes when a human subject is represented in the painting. On the contrary, bottom-up processes, mediated by low-level visual features, particularly affected gazing behavior when looking at nature-content images. We discuss our results proposing a reconsideration of the definition of content-related top-down processes in accordance with the concept of embodied simulation in art perception.

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Section: Discussionsupporting

confidence: 90%

When Art Moves the Eyes: A Behavioral and Eye-Tracking Study

et al. 2012

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“…As can be seen in Table 1 , low-level image features account for a significant proportion of variance in preference [adjusted R 2 = 0.31, F (10,296) = 14.41, P < 0.05]. This result is comparable to others in this area (e.g., about 25% of variance of image preference judgments captured by statistics like sparseness for art images: Graham et al, 2010 ). Standardized regression coefficients and their confidence intervals show that lower hue and more saturation diversity in the image predict more preference while a greater number of straight edges predicts lower preference.…”

Section: Resultssupporting

confidence: 52%

Is the preference of natural versus man-made scenes driven by bottom–up processing of the visual features of nature?

et al. 2015

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Previous research has shown that viewing images of nature scenes can have a beneficial effect on memory, attention, and mood. In this study, we aimed to determine whether the preference of natural versus man-made scenes is driven by bottom–up processing of the low-level visual features of nature. We used participants’ ratings of perceived naturalness as well as esthetic preference for 307 images with varied natural and urban content. We then quantified 10 low-level image features for each image (a combination of spatial and color properties). These features were used to predict esthetic preference in the images, as well as to decompose perceived naturalness to its predictable (modeled by the low-level visual features) and non-modeled aspects. Interactions of these separate aspects of naturalness with the time it took to make a preference judgment showed that naturalness based on low-level features related more to preference when the judgment was faster (bottom–up). On the other hand, perceived naturalness that was not modeled by low-level features was related more to preference when the judgment was slower. A quadratic discriminant classification analysis showed how relevant each aspect of naturalness (modeled and non-modeled) was to predicting preference ratings, as well as the image features on their own. Finally, we compared the effect of color-related and structure-related modeled naturalness, and the remaining unmodeled naturalness in predicting esthetic preference. In summary, bottom–up (color and spatial) properties of natural images captured by our features and the non-modeled naturalness are important to esthetic judgments of natural and man-made scenes, with each predicting unique variance.

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“…A potential strategy for estimating similarity across different clusters, such as red and blue points in a scatterplot, might involve computing ensembles within spatial or featural clusters (Corbett & Melcher, 2014) and then comparing those statistics between clusters (Dakin, 2014). This strategy relies on comparing ensembles as opposed to detailed patterns to estimate similarity across different subsets of data and correlates well with how viewers perceive similarity between pieces of artwork, another type of complex visual scene-here, perceived similarity correlates with comparisons between mean luminances of corresponding spatial regions in a painting (Graham, Friedenberg, McCandless, & Rockmore, 2010).…”

Section: Similarity Detectionmentioning

confidence: 99%

Four types of ensemble coding in data visualizations

et al. 2016

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Ensemble coding supports rapid extraction of visual statistics about distributed visual information. Researchers typically study this ability with the goal of drawing conclusions about how such coding extracts information from natural scenes. Here we argue that a second domain can serve as another strong inspiration for understanding ensemble coding: graphs, maps, and other visual presentations of data. Data visualizations allow observers to leverage their ability to perform visual ensemble statistics on distributions of spatial or featural visual information to estimate actual statistics on data. We survey the types of visual statistical tasks that occur within data visualizations across everyday examples, such as scatterplots, and more specialized images, such as weather maps or depictions of patterns in text. We divide these tasks into four categories: identification of sets of values, summarization across those values, segmentation of collections, and estimation of structure. We point to unanswered questions for each category and give examples of such cross-pollination in the current literature. Increased collaboration between the data visualization and perceptual psychology research communities can inspire new solutions to challenges in visualization while simultaneously exposing unsolved problems in perception research.

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Preference for art: similarity, statistics, and selling price

Cited by 18 publications

References 36 publications

When Art Moves the Eyes: A Behavioral and Eye-Tracking Study

When Art Moves the Eyes: A Behavioral and Eye-Tracking Study

Is the preference of natural versus man-made scenes driven by bottom–up processing of the visual features of nature?

Four types of ensemble coding in data visualizations

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